Guy Kawasaki

I was in a boardroom last week when an executive confidently said, "We need AI agents for our agentic AI strategy." Everyone nodded. Almost no one knew what it meant. Here's what I'm seeing with the most successful AI implementations: Leaders who understand the nuances get better results. Let’s clear something up: • AI agents ≠ Agentic AI • Applied AI ≠ Generative AI These aren’t semantic differences. They shape how you extract value from AI. These distinctions matter because they unlock specific value for your business. If you confuse applied AI with generative AI, you risk overpromising - like promising a smart conversational assistant and delivering a system that can only generate structured reports. When you understand the difference between applied AI and generative AI, you can set the right expectations. You can measure meaningful outcomes. The organizations seeing the biggest wins from AI? They match the right AI approach to the right business challenge. So before your next AI initiative, ask: 1. What specific type of AI will serve our needs best? 2. How will THIS type of AI solve our business challenge? 3. What measurable outcomes can we expect? AI literacy empowers you to make strategic decisions that drive real business impact. What’s one AI use case you’ve implemented that actually moved the needle?

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I've worked with Bruno Aziza at both Google and IBM. He's got a good post this week on his Substack outlining learnings from working with me and my stuff (link in the comments). I love his point about Positioning being mischaracterized as a marketing exercise. I like to think of the components of positioning as inputs to: ## Marketing - messaging for sure, but also segmentation and campaign strategy ## Sales - in particular sales enablement, and account targeting. ## Go to Market strategy - which of course includes elements of the two above points, but also the longer-term strategy around how our segments and value might change over time.

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Too often, the debate around AI in the workplace is framed around replacement rather than empowerment. From my perspective, the technology has the potential to do much more than remove repetitive work: it can guide, support, and even coach the next generation of professionals. In my recent piece for Business Reporter, I shared why cutting graduate roles in response to automation is, in my view, a short-sighted move. Entry-level positions have always been the foundation for developing expertise, and without them, firms risk weakening the very talent pipeline that secures long-term success. By embedding AI in the right way, junior staff can be freed from low-value tasks and supported to accelerate their learning. More importantly, AI can be trained by senior professionals to capture and share their expertise, turning it into a coach that helps the next generation grow and take on meaningful responsibilities earlier in their careers. For me, AI is not about pushing people aside, but about creating the conditions for knowledge to be passed on more effectively, so that individuals can do their best work and develop into the experts that clients and organisations rely on. Read the full article below ⬇️

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Today's update on the challenges facing #GenAI adoption and enterprise #scaling revisits the brewing crisis and further raises the issue of skills shortage, vendor lock-in, and the elusive #ROI of adopting AI. In my research, I identified a lack of business value as a key impediment and the new skills around #CognitiveCollaboration and "Probabilistic Problem Solving" as the critical skills in the AI age. But the main stories first: --- AI Enterprise Scaling: The ROI Crisis Deepens This week's updates show enterprise AI struggling with systemic issues beyond technology, creating a crisis of confidence and hindering measurable ROI. Update 1: The Visibility Gap Crisis Challenge: Massive underestimation of actual AI usage and costs. Details: According to new research, IT leaders underestimate application usage by 1,600%. This creates a massive blind spot for AI tool engagement, costing companies millions in inefficiencies and making true ROI measurement nearly impossible. Source: https://lnkd.in/gaPqywbP Update 2: The Skills Crisis Reaches a Breaking Point Challenge: Critical talent shortages are derailing transformation timelines. Details: A staggering 87% of organizations now report skills gaps, a problem projected to cost the global economy $5.5 trillion. Despite high demand for AI/ML roles, only 28% of companies have achieved adequate data literacy, stalling deployments. Source: https://lnkd.in/g2GYGngG Update 3: The Vendor Lock-in Epidemic Challenge: Vendor lock-in is actively destroying AI value. Details: AI project abandonment rates have soared to 42%. This is linked to vendor-led pilots that create dependency without proving value, trapping companies in solutions that fail to integrate with core business workflows or deliver on ROI promises. Source: https://lnkd.in/ge85hWT6 Key Takeaway The AI scaling crisis is now more organizational than technical. Success hinges on clear ROI visibility, proactive workforce development, and vendor-independent strategies that prioritize proven business value over technology.  --- Those interested in exploring the Theory of #CognitiveChasms are welcome to check out my work at https://lnkd.in/gjsb4H-7. Cognitive Chasm

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Nobody is telling the truth about how disruptive this is going to be. Maybe since I’m ‘unaffiliated’ for now, I can. 👀 AI isn’t just coming for workflows. It’s coming for the foundations of knowledge work — and for the identities built on it. For CMOs and marketing leaders, this hits hard. For years, we’ve found meaning in our playbooks and how we execute. Now, much of that is being automated, flattened, or redefined in real time. And here’s the part we’re not talking about enough: This isn’t just a professional disruption. It’s a personal one. To survive it — let alone lead through it and flourish — we can’t keep pushing at the same velocity, hoping to outrun the change. We need to slow down. Reground. Rebuild. That means investing differently: Mind – Slowing down to build emotional resilience in a time of accelerated everything. Learning to lead from clarity, not reaction. Body – Remembering we’re not brains on a stick. Stewarding our embodied experience through sleep, movement, and nourishment — so we can actually sustain the work. Soul – Recognizing that our sense of identity is often wrapped up in our work — and that AI will inevitably challenge how we define purpose and value. This season demands we examine those foundations, and do so in community, not isolation. This is the real work of leadership in the Intelligence Age. Not just learning to prompt better. But learning to stay human — when everything else is speeding up.

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The AI skill gap is exploding. It's separating winners from everyone else. If you're leading a team, a department, or an entire company, your AI fluency will either accelerate your impact or hold it back. These skills aren’t just technical. They’re strategic levers for growth, efficiency, and resilience. Here are 11 essential AI skills every leader needs: 1/ Understanding Prompt Engineering ↳ Craft precise AI instructions ↳ Generate consistent outputs 💡 Pro tip: Keep a "prompt library" of your most effective prompts. Document what works and why. 2/ AI Risk Assessment ↳ Spot hallucinations early ↳ Set guardrails for teams 💡 Pro tip: Run monthly "red team" exercises where your team tries to break your AI systems. 3/ Data Quality Management ↳ Validate training inputs ↳ Monitor data drift 💡 Pro tip: Create automated data quality scorecards that flag issues before they impact operations. 4/ AI Tool Selection ↳ Match tools to problems ↳ Scale solutions effectively 💡 Pro tip: Start with a 30-day pilot on non-critical tasks before full implementation. 5/ Ethical AI Governance ↳ Implement fair practices ↳ Protect user privacy 💡 Pro tip: Build an "AI Ethics Checklist" and review it before every new AI deployment. 6/ Integration Strategy ↳ Connect AI systems ↳ Optimize workflows 💡 Pro tip: Map your entire process flow before integration. It's easier to spot optimization opportunities. 7/ ROI Measurement ↳ Track AI investments ↳ Measure productivity gains 💡 Pro tip: Measure both quantitative (time/money) and qualitative (team satisfaction) metrics. 8/ AI Team Leadership ↳ Bridge tech-business gap ↳ Upskill existing teams 💡 Pro tip: Schedule bi-weekly "AI Office Hours" where teams can ask questions and share wins. 9/ Change Management ↳ Navigate AI transitions ↳ Build AI-positive culture 💡 Pro tip: Celebrate small AI wins publicly. Success stories drive adoption better than mandates. 10/ Compliance Navigation ↳ Stay regulation-aware ↳ Maintain audit trails 💡 Pro tip: Build relationships with your legal team early. They should be partners, not roadblocks. 11/ Future-State Planning ↳ Plan capability roadmaps ↳ Build adaptive systems 💡 Pro tip: Reserve 20% of your AI budget for experimenting with emerging technologies. Mastering these AI skills isn’t about keeping up but about staying ahead. The leaders who invest in these capabilities now will be the ones shaping the competitive landscape in 2026 and beyond. Start small, stay consistent, and build momentum. Which skill are you focusing on first? Share below 👇 — ♻️ Repost if your network needs this checklist. Looking to learn how to leverage AI to improve your leadership skills? DM me to discuss how I can help you get started.

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